scholarly journals Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith observations under snow conditions

2017 ◽  
Vol 10 (1) ◽  
pp. 155-165 ◽  
Author(s):  
Wengang Zhang ◽  
Guirong Xu ◽  
Yuanyuan Liu ◽  
Guopao Yan ◽  
Dejun Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Microwave Radiometer ◽  
Retrieval Algorithm ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Vapor Density ◽  
Heavy Snowfall ◽  
Snow Conditions ◽  
The Impact ◽  
Mean Square Errors

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith observations. The MWR retrievals were averaged in a ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith observations than in zenith observations, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith observations. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith observations to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith observations, respectively. The discrepancies between MWR retrievals and RAOB profiles by altitude present the same situation. Cases studies show that the impact of snow on accuracies of MWR retrievals is more serious in heavy snowfall than in light snowfall, but off-zenith observation can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall mainly due to the retrieval algorithm, which does not consider the effect of snow, and the accumulated snow on the top of the radome increases the signal noise of MWR measurements. As the snowfall drops away by gravity on the sides of the radome, the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

scholarly journals Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith methods under snow conditions

2016 ◽  
Author(s):  
Wengang Zhang ◽  
Guirong Xu ◽  
Yuanyuan Liu ◽  
Guopao Yan ◽  
Shengbo Wang
Keyword(s):  
Relative Humidity ◽  
Root Mean Square ◽  
Microwave Radiometer ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Vapor Density ◽  
Heavy Snowfall ◽  
Snow Conditions ◽  
The Impact ◽  
Mean Square Errors

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith methods. The MWR retrievals were averaged in the ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with the radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith method than in zenith method, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith method. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith method to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith method, respectively. The discrepancies between the MWR retrievals and the RAOB profiles along with the altitude present the same situation. Case studies show that the impact of snow on accuracies of the MWR retrievals is more serious in heavy snowfall than that in light snowfall, but the off-zenith method can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall is mainly due to the retrieving method which does not consider the effect of snow, and the accumulated snow on the top of radome increases the signal noise of MWR measurement. As the snowfall drops away by gravity in the sides of the radome and the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

The Impact of Atmospheric Conditions on Gas Turbine Performance

1990 ◽  
Vol 112 (4) ◽  
pp. 590-596 ◽  
Author(s):  
A. A. El Hadik
Keyword(s):  
Relative Humidity ◽  
Ambient Temperature ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Arabian Gulf ◽  
Inlet Temperature ◽  
Atmospheric Conditions ◽  
Design Factor ◽  
Turbine Performance ◽  
The Impact

In a hot summer climate, as in Kuwait and other Arabian Gulf countries, the performance of a gas turbine deteriorates drastically during the high-temperature hours (up to 60°C in Kuwait). Power demand is the highest at these times. This necessitates an increase in installed gas turbine capacities to balance this deterioration. Gas turbines users are becoming aware of this problem as they depend more on gas turbines to satisfy their power needs and process heat for desalination due to the recent technical and economical development of gas turbines. This paper is devoted to studying the impact of atmospheric conditions, such as ambient temperature, pressure, and relative humidity on gas turbine performance. The reason for considering air pressures different from standard atmospheric pressure at the compressor inlet is the variation of this pressure with altitude. The results of this study can be generalized to include the cases of flights at high altitudes. A fully interactive computer program based on the derived governing equations is developed. The effects of typical variations of atmospheric conditions on power output and efficiency are considered. These include ambient temperature (range from −20 to 60°C), altitude (range from zero to 2000 m above sea level), and relative humidity (range from zero to 100 percent). The thermal efficiency and specific net work of a gas turbine were calculated at different values of maximum turbine inlet temperature (TIT) and variable environmental conditions. The value of TIT is a design factor that depends on the material specifications and the fuel/air ratio. Typical operating values of TIT in modern gas turbines were chosen for this study: 1000, 1200, 1400, and 1600 K. Both partial and full loads were considered in the analysis. Finally the calculated results were compared with actual gas turbine data supplied by manufacturers.

scholarly journals Biogeography of Tropical Montane Cloud Forests. Part I: Remote Sensing of Cloud-Base Heights

2008 ◽  
Vol 47 (4) ◽  
pp. 960-975 ◽  
Author(s):  
Ronald M. Welch ◽  
Salvi Asefi ◽  
Jian Zeng ◽  
Udaysankar S. Nair ◽  
Qingyuan Han ◽  
...  
Keyword(s):  
Temperature Profile ◽  
Liquid Water ◽  
Cloud Base ◽  
Retrieval Algorithm ◽  
Mean Square ◽  
Cloud Forests ◽  
Orographic Clouds ◽  
Tropical Montane Cloud Forests ◽  
Mean Square Errors ◽  
Cloud Thickness

Abstract Cloud-base heights over tropical montane cloud forests are determined using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products and National Centers for Environmental Prediction global tropospheric final analysis (FNL) fields. Cloud-base heights are computed by subtracting cloud thickness estimates from cloud-top height estimates. Cloud-top pressures determined from the current MODIS retrieval algorithm often have serious cloud-top pressure retrieval errors at pressures > 700 hPa. The problem can be easily remedied by matching cloud-top temperature derived from the 11-μm channel to the dewpoint temperature profile (instead of the temperature profile) obtained from the FNL dataset. The FNL dataset at 1° spatial resolution produced results that were nearly equivalent to those derived from radiosonde measurements. The following three different approaches for estimating cloud thickness are examined: 1) the constant liquid water method, 2) the empirical method, and 3) the adiabatic model method. The retrieval technique is applied first for stratus clouds over U.S. airports for 12 cases, with cloud-base heights compared with ceilometer measurements. Mean square errors on the order of 200 m result. Then, the approach is applied to orographic clouds over Monteverde, Costa Rica, with estimated cloud-base heights compared with those derived from photographs. Mean square errors on the order of 100 m result. Both the empirical and adiabatic model approaches produce superior results when compared with the constant liquid water (CLW) approach. This is due to the fact that CLW is more sensitive to natural variations in cloud optical thickness.

scholarly journals Minimum Local Emissivity Variance Retrieval of Cloud Altitude and Effective Spectral Emissivity—Simulation and Initial Verification

2004 ◽  
Vol 43 (5) ◽  
pp. 795-809 ◽  
Author(s):  
Hung-Lung Huang ◽  
William L. Smith ◽  
Jun Li ◽  
Paolo Antonelli ◽  
Xiangqian Wu ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Spectral Resolution ◽  
Single Layer ◽  
Spectral Emissivity ◽  
Pressure Level ◽  
High Spectral Resolution ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Wide Range ◽  
Mean Square Errors

Abstract This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes of clouds exhibit relatively limited localized spectral emissivity structure in the infrared 10–15-μm longwave region. In this simulation study it is shown that the MLEV cloud pressure root-mean-square errors for a single level with effective cloud emissivity greater than 0.1 are ∼30, ∼10, and ∼50 hPa, for high (200– 300 hPa), middle (500 hPa), and low (850 hPa) clouds, respectively. The associated cloud emissivity root-mean-square errors in the 900 cm−1 spectral channel are less than 0.05, 0.04, and 0.25 for high, middle, and low clouds, respectively.

scholarly journals The influence of atmospheric conditions on the migration of diesel fuel spilled in soil

2017 ◽  
Vol 43 (1) ◽  
pp. 73-79
Author(s):  
Mladen Vuruna ◽  
Zlate Veličković ◽  
Sreten Perić ◽  
Jovica Bogdanov ◽  
Negovan Ivanković ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Diesel Fuel ◽  
Soil Depth ◽  
Petroleum Products ◽  
Atmospheric Conditions ◽  
Humidity Effect ◽  
Relative Humidity Effect ◽  
Fuel Evaporation ◽  
The Impact

Abstract The most common chemical’s spills in typical transportation accidents are those with petroleum products such as diesel fuel, the consequence of which is an extensive pollution of the soil. In order to plan properly fuel recovery from the soil, it is important to gain information about the soil depth which may be affected by pollutant and to predict the pollutant concentration in different soil layers. This study deals with the impact of basic atmospheric conditions, i.e. air temperature and humidity on the diesel fuel migration through the soil. The diesel fuel was spilled into columns (L = 30 cm; D = 4.6 cm) filled with sandy and clay soil samples, and its concentrations at various depths were measured after 11 days under various air temperature (20 and 40°C) and relative humidity (30–100%) conditions. The effects observed were explained by understanding physical processes, such as fuel evaporation, diffusion and adsorption on soil grains. The increase in temperature results in higher fuel evaporation loss and its faster vertical migration. The relative humidity effect is less pronounced but more complex, and it depends much on the soil type.

The Analysis of Atmospheric Water Vapor Based on Ground-Based Microwave Radiometer

2012 ◽  
Vol 500 ◽  
pp. 335-340
Author(s):  
Jie Ying He ◽  
Feng Lin Sun ◽  
Sheng Wei Zhang ◽  
Yu Zhang
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Microwave Radiometer ◽  
Mean Square ◽  
Vapor Density ◽  
China Meteorological Administration ◽  
Density Profiles

The paper introduces a widely used atmospheric absorption models: MPM by Liebe in 1989. Using this absorption model, the paper simulates the temperature and humidity weighting functions and brightness temperature according to the different frequencies and bandwidth of the multi-channel ground-based microwave radiometer. The results show that simulated brightness temperatures are very well agreement with the observation values with an acceptable root mean square error. This paper uses widely used retrieval method of artificial neural network to obtain the water vapor density profiles and calculates the root mean square error of each dataset. Also, to improve the accuracy of retrievals, this paper adopts multi-layers neural network which has two hidden layers. The results show that the retrievals of water vapor density profiles based on ground-based microwave radiometer are agreement with the water vapor density profile which is observed by radiosonde. Grant Nos. GYHY200906035 China Meteorological Administration nonprofit sector (meteorology) special research

A Study of a Retrieval Method for Temperature and Humidity Profiles from Microwave Radiometer Observations Based on Principal Component Analysis and Stepwise Regression

2011 ◽  
Vol 28 (3) ◽  
pp. 378-389 ◽  
Author(s):  
Haobo Tan ◽  
Jietai Mao ◽  
Huanhuan Chen ◽  
P. W. Chan ◽  
Dui Wu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Water Vapor ◽  
Stepwise Regression ◽  
Microwave Radiometer ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Vapor Density ◽  
Retrieval Method ◽  
Brightness Temperatures

Abstract This paper discusses the application of principal component analysis and stepwise regression in the retrieval of vertical profiles of temperature and humidity based on the measurements of a 35-channel microwave radiometer. It uses the radiosonde data of 6 yr from Hong Kong, China, and the monochromatic radiative transfer model (MonoRTM) to calculate the brightness temperatures of the 35 channels of the radiometer. The retrieval of the atmospheric profile is then established based on principal component analysis and stepwise regression. The accuracy of the retrieval method is also analyzed. Using an independent sample, the root-mean-square error of the retrieved temperature is less than 1.5 K, on average, with better retrieval results in summer than in winter. Likewise, the root-mean-square error of the retrieved water vapor density reaches a maximum value of 1.4 g m−3 between 0.5 and 2 km, and is less than 1 g m−3 for all other heights. The retrieval method is then applied to the actual measured brightness temperatures by the 35-channel microwave radiometer at a station in Nansha, China. It is shown that the statistical model as developed in this paper has better retrieval results than the profiles obtained from the neural network as supplied with the radiometer. From numerical analysis, the error with the water vapor density retrieval is found to arise from the treatment of cloud liquid water. Finally, the retrieved profiles from the radiometer are studied for two typical weather phenomena during the observation period, and the retrieved profiles using the method discussed in the present paper is found to capture the evolution of the atmospheric condition very well.

scholarly journals Impact of temperature and relative humidity on the transmission of COVID-19: a modelling study in China and the United States

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e043863
Author(s):  
Jingyuan Wang ◽  
Ke Tang ◽  
Kai Feng ◽  
Xin Lin ◽  
Weifeng Lv ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Relative Humidity ◽  
Human Mobility ◽  
The United States ◽  
Reproductive Number ◽  
Cross Sectional ◽  
The Usa ◽  
Higher Temperature ◽  
Potential Impact ◽  
The Impact

ObjectivesWe aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status and human mobility status.DesignA retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted.SettingWe use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1005 US counties.ParticipantsA total of 69 498 cases in China and 740 843 cases in the USA are used for calculating the effective reproductive numbers.Primary outcome measuresRegression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).ResultsStatistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the USA.ConclusionsHigher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1°C is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI (−0.0395 to −0.0125)) in China and by 0.020 (95% CI (−0.0311 to −0.0096)) in the USA; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI (−0.0108 to −0.0045)) in China and by 0.0080 (95% CI (−0.0150 to −0.0010)) in the USA. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic.

scholarly journals Evolution of the viscoelastic properties of painting stratigraphies: a moisture weathering and nanoindentation approach

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Mathilde Tiennot ◽  
Davide Iannuzzi ◽  
Erma Hermens
Keyword(s):  
Mechanical Properties ◽  
Relative Humidity ◽  
Cultural Heritage ◽  
Mechanical Behaviour ◽  
Viscoelastic Properties ◽  
Viscoelastic Behaviour ◽  
Storage And Loss Moduli ◽  
Heritage Studies ◽  
Paint Films ◽  
The Impact

AbstractIn this investigation on the mechanical behaviour of paint films, we use a new ferrule-top nanoindentation protocol developed for cultural heritage studies to examine the impact of repeated relative humidity variations on the viscoelastic behaviour of paint films and their mechanical properties in different paint stratigraphies through the changes in their storage and loss moduli. We show that the moisture weathering impact on the micromechanics varies for each of these pigment-oil systems. Data from the nanoindentation protocol provide new insights into the evolution of the viscoelastic properties dsue to the impact of moisture weathering on paint films.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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